AI Grading Automation Workflows
A version-controlled n8n workflow system for multi-agent, human-in-the-loop grading automation across rubrics, submission processing, LLM evaluation, approval, feedback, and audit logging.
Project Architecture Artifacts
Comprehensive engineering blueprints, economic analyses, and software lifecycle documentation structured during the 2025-2 developmental cycle:
Business Case
Financial ROI assessments, institution delivery latency vectors, and multi-tenant scaling market validation charts.
PDD + SDD
Process Definition Document combined with System Design Document mapping strict API interaction maps and nodes.
Memory Design
Low-level structural specs for transient storage layer mechanics, Supabase state management, and semantic retrieval caching.
ECI Tech Innovate Summit 2025
Hyperautomation Track Live Demonstration: This playback segment (00:45:50 to 01:09:50)features the core engineering team presenting the system's conceptual framework, multi-agent mesh, and live execution triggers.
Background
Traditional grading is fundamentally broken: instructors routinely exhaust 6 to 8 hours manually reviewing single assignment lots. This creates operational friction characterized by delayed feedback intervals, fatigue-induced scoring variances, and lack of systemic auditing metrics during the 2025-2 semester evaluations.
The AI Grading Automation System mitigates these systemic dependencies by wrapping structural steps in an autonomous, auditable, and human-verified workspace routing matrix.
System Installation & Environment Setup
Workflows are bundled as standardized, version-controlled JSON instances ready to import into your n8n node graph.
git clone https://github.com/LePeanutButter/ai-grading-automation-workflows-backup.git cd ai-grading-automation-workflows-backup
Ensure active target environment variable bindings for Google Workspace, OpenAI/Gemini APIs, and Supabase relational endpoints.
Distributed Multi-Agent Architecture
Instead of single linear scripts, the engine acts as an event-driven mesh where micro-workflows function as specialized cognitive entities:
Rubric Agent
Adapts, maps, and validates structural evaluations based on custom pedagogical parameters.
Evaluation Agent
Processes target student outputs to yield robust qualitative breakdowns and numeric approximations.
Document Agent
Coordinates multi-format raw extraction layers, file parsing, and optical character recognition (OCR).
Memory Agent
Maintains hot cross-session context, persistent evaluation trends, and profile-based biases.
Compliance Agent
Inspects telemetry arrays, executing raw PII tokenization and data anonymization masks.
Instructor Agent
Publishes intermediate secure state targets awaiting manual reviewer approval.
Human-in-the-Loop (HITL) Guardrails
Academic accountability is non-negotiable. The platform restricts direct deployment workflows: all system-generated feedback matrices are frozen inside structural holding queues until explicitly approved or mutated by the instructor.
Hyperautomation Lifecycle (HAL) Matrix
The operational framework structures grading scaling via a complete end-to-end implementation lifecycle:
Discover
Isolate latency limits and evaluation bottlenecks.
Analyze
Map dependencies and processing criteria graphs.
Design
Model node logic boundaries and agent handoffs.
Automate
Deploy persistent multi-service n8n flow networks.
Orchestrate
Synchronize cross-system APIs seamlessly.
Optimize
Refine execution token costs and response precision.
Govern
Enforce encryption rules and structural audit logs.
Enterprise Security & Compliance
Every data boundary is isolated to support institutional standards:
- Credential Management: Secrets are strictly non-exposed, utilizing encrypted native n8n Credential Storage keys.
- Anonymization Pipelines: Ingestion nodes scrub identifying data parameters prior to external infrastructure routing.
- Immutable Audit Traces: Operational logs register every state mutation, change, and approval loop.
Source code
Explore the repositories that implement this project:
B2C Model
Direct-to-Teacher Utility: Frictionless individual onboarding with low transactional overhead. Built to return immediate time-equity directly to teachers managing individual academic classrooms.
B2B Model
Institutional Enterprise Licensing: Campus-wide network deployment integrations featuring deep Learning Management System mappings (Canvas, Blackboard, Moodle) and aggregate analytics dashboards.
